Vehicle object detection approach in drone imagery based on improved YOLOv8s

doi:10.13474/j.cnki.11-2246.2026.0211

Abstract

Abstract: Accurate and real-time vehicle detection and tracking provide crucial data support for traffic flow estimation and intelligent traffic management.Drone imagery has emerged as a vital data source for vehicle detection tasks.To address the weak ability of existing YOLO models to detect small objects within complex scenarios and the scarcity of open-source datasets for drone vehicle detection,this paper proposes the YOLOv8s-VOD model specifically designed for vehicle detection tasks,and introduces the open-source dataset NJVOD.This method constructs C2f-PTB and BiFPN-GLSA modules to achieve collaborative extraction of global-local featuresand effective fusion of multi-scale semantic and edge information,thereby improving detection accuracywhile reducing network complexity.Experimental results show that YOLOv8s-VOD achieves the highest detection accuracy with minimal parameters,outperforming existing methods by 2.4～12.2 percentage poin on the VEDAI dataset and 4.1～5.3 percentage point on the NJVOD dataset;The C2f-PTB and BiFPN-GLSA modules proposed in this work both effectively improve small-object detection accuracy.Additionally,the newly created NJVOD dataset offers crucial support for related research.

Key words: YOLOv8, drone imagery, vehicle detection, attention mechanism, feature fusion

CLC Number:

P237

TENG Min, ZHANG Bo, XU Jiawei, LIN Cong, SHEN Yu, CHU Zhengwei. Vehicle object detection approach in drone imagery based on improved YOLOv8s[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 68-73,80.

References

[1] 赵娜,袁家斌,徐晗.智能交通系统综述[J].计算机科学,2014,41(11):7-11.
[2] 刘勃.交通流量的视频检测方法研究[D].合肥:中国科学技术大学,2005.
[3] 李爽.基于视觉时空信息的城市交通流估计方法研究[D].济南:山东大学,2021.
[4] 李德仁,李明.无人机遥感系统的研究进展与应用前景[J].武汉大学学报(信息科学版),2014,39(5):505-513.
[5] 奉志强,谢志军,包正伟,等.基于改进YOLOv5的无人机实时密集小目标检测算法[J].航空学报,2023,44(7):327106.
[6] BENSRHAIR A,BERTOZZI M,BROGGI A,et al.A cooperative approach to vision-based vehicle detection[C]//Proceedings of 2001 IEEE Intelligent TransportationSystems.Oakland,CA:IEEE,2002:207-212.
[7] COLLADO J M,HILARIO C,DELAESCALERAA,et al.Model based vehicle detection for intelligent vehicles[C]//Proceedings of 2004 IEEE Intelligent Vehicles Symposium.Parma:IEEE,2004:572-577.
[8] 钱志明,杨家宽,段连鑫.基于视频的车辆检测与跟踪研究进展[J].中南大学学报(自然科学版),2013,44(S2):222-227.
[9] ALSANABANI A A,SAEED S A,AL-MKHLAFI M,et al.A low cost and real time vehicle detection using enhanced YOLOv4-tiny[C]//Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA).Dalian:IEEE,2021:372-377.
[10] FENG Jiangfan,YI Chengjie.Lightweight detection network for arbitrary-oriented vehicles in UAV imagery via global attentive relation and multi-path fusion[J].Drones,2022,6(5):108.
[11] LIU Xiaojia,WANG Yipeng,YU Dexin,et al.YOLOv8-FDD:a real-time vehicle detection method based on improved YOLOv8[J].IEEE Access,2024,12:136280-136296.
[12] RAZAKARIVONY S,JURIE F.Vehicle detection in aerial imagery:a small target detection benchmark[J].Journal of Visual Communication and Image Representation,2016,34:187-203.
[13] TAN Mingxing,PANG Ruoming,LE Q V.EfficientDet:scalable and efficient object detection[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle,WA:IEEE,2020:10778-10787.
[14] REDMON J,FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2025-04-10].https://arxiv.org/abs/1804.02767.
[15] LI Chuyi,LI Lulu,JIANG Hongliang,et al.YOLOv6:a single-stage object detection framework for industrial applications[EB/OL].[2025-04-10].https://arxiv.org/abs/2209.02976.